Predictive models for strain energy in condensed phase reactions

TL;DR

This paper introduces a graph neural network model that predicts strain energy in condensed-phase reactions, improving the accuracy of reaction rate estimations in molecular simulations of thermally activated processes.

Contribution

The study develops a novel GNN-based approach to predict strain energy from local environments, enhancing reaction modeling in condensed-phase molecular simulations.

Findings

The model accurately predicts strain energy from local molecular environments.

Incorporating the model improves reaction rate estimations in MD simulations.

The approach advances understanding of thermally activated reactions in complex materials.

Abstract

Molecular modeling of thermally activated chemistry in condensed phases is essential to understand polymerization, depolymerization, and other processing steps of molecular materials. Current methods typically combine molecular dynamics (MD) simulations to describe short-time relaxation with a stochastic description of predetermined chemical reactions. Possible reactions are often selected on the basis of geometric criteria, such as a capture distance between reactive atoms. Although these simulations have provided valuable insight, the approximations used to determine possible reactions often lead to significant molecular strain and unrealistic structures. We show that the local molecular environment surrounding the reactive site plays a crucial role in determining the resulting molecular strain energy and, in turn, the associated reaction rates. We develop a graph neural network capable of predicting the strain energy associated with a cyclization reaction from the pre-reaction, local, molecular environment surrounding the reactive site. The model is trained on a large dataset of condensed-phase reactions during the activation of polyacrylonitrile (PAN) obtained from MD simulations and can be used to adjust relative reaction rates in condensed systems and advance our understanding of thermally activated chemical processes in complex materials